We develop an algorithm for continual detection of emerging novel types of face PAs. Our objective is to enable the system to identify novel PAs. The model then is updated to learn new attack types such that it does not forget the past learned attack types. Our idea is based on enabling the network to identify new attack types as testing samples that are outside the training distribution samples (OTDS) in an embedding space [1]}, [2]}, [3]}, [4]}, [5]}. The base model is then updated to classify these samples as new attacks types in a continual learning (CL) setting, where catastrophic forgetting [6]} is addressed using experience replay [7]}. Despite being effective in continual learning (CL) settings, the idea of detecting OTDS has not been explored for PAD.
